Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations37496
Missing cells0
Missing cells (%)0.0%
Duplicate rows2650
Duplicate rows (%)7.1%
Total size in memory32.9 MiB
Average record size in memory920.4 B

Variable types

Categorical17
Numeric13
Text1
DateTime1

Alerts

hotel has constant value "City Hotel" Constant
arrival_date_year has constant value "2016" Constant
Dataset has 2650 (7.1%) duplicate rowsDuplicates
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
cancellation_ratio is highly overall correlated with previous_cancellationsHigh correlation
deposit_type is highly overall correlated with is_canceledHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
is_canceled is highly overall correlated with deposit_type and 1 other fieldsHigh correlation
is_repeated_guest is highly overall correlated with previous_bookings_not_canceledHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
previous_bookings_not_canceled is highly overall correlated with is_repeated_guestHigh correlation
previous_cancellations is highly overall correlated with cancellation_ratioHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
stays_in_week_nights is highly overall correlated with total_stayHigh correlation
stays_in_weekend_nights is highly overall correlated with total_stayHigh correlation
total_stay is highly overall correlated with stays_in_week_nights and 1 other fieldsHigh correlation
adults is highly imbalanced (53.2%) Imbalance
children is highly imbalanced (77.9%) Imbalance
babies is highly imbalanced (97.9%) Imbalance
meal is highly imbalanced (53.9%) Imbalance
distribution_channel is highly imbalanced (66.1%) Imbalance
is_repeated_guest is highly imbalanced (86.8%) Imbalance
reserved_room_type is highly imbalanced (62.0%) Imbalance
assigned_room_type is highly imbalanced (55.3%) Imbalance
deposit_type is highly imbalanced (60.3%) Imbalance
customer_type is highly imbalanced (61.1%) Imbalance
required_car_parking_spaces is highly imbalanced (90.0%) Imbalance
previous_bookings_not_canceled is highly skewed (γ1 = 20.69045505) Skewed
lead_time has 1255 (3.3%) zeros Zeros
stays_in_weekend_nights has 18014 (48.0%) zeros Zeros
stays_in_week_nights has 2366 (6.3%) zeros Zeros
previous_cancellations has 36039 (96.1%) zeros Zeros
previous_bookings_not_canceled has 36742 (98.0%) zeros Zeros
booking_changes has 32735 (87.3%) zeros Zeros
days_in_waiting_list has 34846 (92.9%) zeros Zeros
total_of_special_requests has 22139 (59.0%) zeros Zeros
cancellation_ratio has 36039 (96.1%) zeros Zeros

Reproduction

Analysis started2025-01-24 18:59:32.016900
Analysis finished2025-01-24 19:00:14.166307
Duration42.15 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

hotel
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
City Hotel
37496 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters374960
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity Hotel
2nd rowCity Hotel
3rd rowCity Hotel
4th rowCity Hotel
5th rowCity Hotel

Common Values

ValueCountFrequency (%)
City Hotel 37496
100.0%

Length

2025-01-24T19:00:14.291864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:14.391730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
city 37496
50.0%
hotel 37496
50.0%

Most occurring characters

ValueCountFrequency (%)
t 74992
20.0%
C 37496
10.0%
i 37496
10.0%
y 37496
10.0%
37496
10.0%
H 37496
10.0%
o 37496
10.0%
e 37496
10.0%
l 37496
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 74992
20.0%
C 37496
10.0%
i 37496
10.0%
y 37496
10.0%
37496
10.0%
H 37496
10.0%
o 37496
10.0%
e 37496
10.0%
l 37496
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 74992
20.0%
C 37496
10.0%
i 37496
10.0%
y 37496
10.0%
37496
10.0%
H 37496
10.0%
o 37496
10.0%
e 37496
10.0%
l 37496
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 74992
20.0%
C 37496
10.0%
i 37496
10.0%
y 37496
10.0%
37496
10.0%
H 37496
10.0%
o 37496
10.0%
e 37496
10.0%
l 37496
10.0%

is_canceled
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
22177 
1
15319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37496
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 22177
59.1%
1 15319
40.9%

Length

2025-01-24T19:00:14.502716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:14.680990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 22177
59.1%
1 15319
40.9%

Most occurring characters

ValueCountFrequency (%)
0 22177
59.1%
1 15319
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22177
59.1%
1 15319
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22177
59.1%
1 15319
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22177
59.1%
1 15319
40.9%

lead_time
Real number (ℝ)

Zeros 

Distinct394
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.31603
Minimum0
Maximum626
Zeros1255
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:14.816814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q124
median73
Q3163
95-th percentile323
Maximum626
Range626
Interquartile range (IQR)139

Descriptive statistics

Standard deviation109.72858
Coefficient of variation (CV)1.0037739
Kurtosis1.5776421
Mean109.31603
Median Absolute Deviation (MAD)59
Skewness1.3489356
Sum4098914
Variance12040.36
MonotonicityNot monotonic
2025-01-24T19:00:15.015893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1255
 
3.3%
1 957
 
2.6%
2 585
 
1.6%
4 544
 
1.5%
3 515
 
1.4%
5 469
 
1.3%
6 411
 
1.1%
7 374
 
1.0%
8 362
 
1.0%
37 316
 
0.8%
Other values (384) 31708
84.6%
ValueCountFrequency (%)
0 1255
3.3%
1 957
2.6%
2 585
1.6%
3 515
1.4%
4 544
1.5%
5 469
 
1.3%
6 411
 
1.1%
7 374
 
1.0%
8 362
 
1.0%
9 315
 
0.8%
ValueCountFrequency (%)
626 30
0.1%
605 30
0.1%
538 17
< 0.1%
531 17
< 0.1%
524 17
< 0.1%
517 17
< 0.1%
510 17
< 0.1%
503 17
< 0.1%
496 17
< 0.1%
489 17
< 0.1%

arrival_date_year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2016
37496 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters149984
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 37496
100.0%

Length

2025-01-24T19:00:15.203278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:15.289085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 37496
100.0%

Most occurring characters

ValueCountFrequency (%)
2 37496
25.0%
0 37496
25.0%
1 37496
25.0%
6 37496
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 37496
25.0%
0 37496
25.0%
1 37496
25.0%
6 37496
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 37496
25.0%
0 37496
25.0%
1 37496
25.0%
6 37496
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 37496
25.0%
0 37496
25.0%
1 37496
25.0%
6 37496
25.0%

arrival_date_month
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
October
4154 
June
3880 
September
3824 
May
3607 
April
3510 
Other values (7)
18521 

Length

Max length9
Median length7
Mean length6.0341103
Min length3

Characters and Unicode

Total characters226255
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOctober
2nd rowFebruary
3rd rowFebruary
4th rowFebruary
5th rowApril

Common Values

ValueCountFrequency (%)
October 4154
11.1%
June 3880
10.3%
September 3824
10.2%
May 3607
9.6%
April 3510
9.4%
August 3348
8.9%
July 3080
8.2%
November 3053
8.1%
March 2990
8.0%
December 2425
6.5%
Other values (2) 3625
9.7%

Length

2025-01-24T19:00:15.403974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
october 4154
11.1%
june 3880
10.3%
september 3824
10.2%
may 3607
9.6%
april 3510
9.4%
august 3348
8.9%
july 3080
8.2%
november 3053
8.1%
march 2990
8.0%
december 2425
6.5%
Other values (2) 3625
9.7%

Most occurring characters

ValueCountFrequency (%)
e 35200
15.6%
r 25894
 
11.4%
u 17281
 
7.6%
b 15769
 
7.0%
a 11534
 
5.1%
t 11326
 
5.0%
y 10312
 
4.6%
c 9569
 
4.2%
m 9302
 
4.1%
J 8272
 
3.7%
Other values (16) 71796
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 226255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 35200
15.6%
r 25894
 
11.4%
u 17281
 
7.6%
b 15769
 
7.0%
a 11534
 
5.1%
t 11326
 
5.0%
y 10312
 
4.6%
c 9569
 
4.2%
m 9302
 
4.1%
J 8272
 
3.7%
Other values (16) 71796
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 226255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 35200
15.6%
r 25894
 
11.4%
u 17281
 
7.6%
b 15769
 
7.0%
a 11534
 
5.1%
t 11326
 
5.0%
y 10312
 
4.6%
c 9569
 
4.2%
m 9302
 
4.1%
J 8272
 
3.7%
Other values (16) 71796
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 226255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 35200
15.6%
r 25894
 
11.4%
u 17281
 
7.6%
b 15769
 
7.0%
a 11534
 
5.1%
t 11326
 
5.0%
y 10312
 
4.6%
c 9569
 
4.2%
m 9302
 
4.1%
J 8272
 
3.7%
Other values (16) 71796
31.7%

arrival_date_week_number
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.863105
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:15.582034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q118
median29
Q341
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.630266
Coefficient of variation (CV)0.47223836
Kurtosis-1.0864238
Mean28.863105
Median Absolute Deviation (MAD)12
Skewness-0.072247581
Sum1082251
Variance185.78414
MonotonicityNot monotonic
2025-01-24T19:00:15.806518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1117
 
3.0%
42 1103
 
2.9%
40 1004
 
2.7%
45 963
 
2.6%
38 950
 
2.5%
21 943
 
2.5%
44 913
 
2.4%
23 908
 
2.4%
24 902
 
2.4%
43 887
 
2.4%
Other values (43) 27806
74.2%
ValueCountFrequency (%)
1 138
 
0.4%
2 187
 
0.5%
3 228
 
0.6%
4 368
1.0%
5 371
1.0%
6 352
0.9%
7 473
1.3%
8 723
1.9%
9 552
1.5%
10 697
1.9%
ValueCountFrequency (%)
53 725
1.9%
52 391
1.0%
51 350
 
0.9%
50 602
1.6%
49 615
1.6%
48 614
1.6%
47 639
1.7%
46 813
2.2%
45 963
2.6%
44 913
2.4%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.889108
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:15.975501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7685032
Coefficient of variation (CV)0.55185622
Kurtosis-1.1847787
Mean15.889108
Median Absolute Deviation (MAD)8
Skewness-0.023669617
Sum595778
Variance76.886649
MonotonicityNot monotonic
2025-01-24T19:00:16.161587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 1469
 
3.9%
28 1450
 
3.9%
20 1437
 
3.8%
13 1426
 
3.8%
19 1379
 
3.7%
21 1359
 
3.6%
26 1320
 
3.5%
7 1308
 
3.5%
2 1303
 
3.5%
6 1273
 
3.4%
Other values (21) 23772
63.4%
ValueCountFrequency (%)
1 1139
3.0%
2 1303
3.5%
3 1155
3.1%
4 1189
3.2%
5 1165
3.1%
6 1273
3.4%
7 1308
3.5%
8 1207
3.2%
9 1147
3.1%
10 1019
2.7%
ValueCountFrequency (%)
31 568
 
1.5%
30 1226
3.3%
29 1199
3.2%
28 1450
3.9%
27 1214
3.2%
26 1320
3.5%
25 1145
3.1%
24 1229
3.3%
23 1011
2.7%
22 1147
3.1%

stays_in_weekend_nights
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78155003
Minimum0
Maximum9
Zeros18014
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:16.325468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87759561
Coefficient of variation (CV)1.1228911
Kurtosis2.5960196
Mean0.78155003
Median Absolute Deviation (MAD)1
Skewness1.0507976
Sum29305
Variance0.77017406
MonotonicityNot monotonic
2025-01-24T19:00:16.462605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 18014
48.0%
1 10410
27.8%
2 8695
23.2%
3 157
 
0.4%
4 146
 
0.4%
5 30
 
0.1%
6 25
 
0.1%
8 11
 
< 0.1%
7 5
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 18014
48.0%
1 10410
27.8%
2 8695
23.2%
3 157
 
0.4%
4 146
 
0.4%
5 30
 
0.1%
6 25
 
0.1%
7 5
 
< 0.1%
8 11
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 11
 
< 0.1%
7 5
 
< 0.1%
6 25
 
0.1%
5 30
 
0.1%
4 146
 
0.4%
3 157
 
0.4%
2 8695
23.2%
1 10410
27.8%
0 18014
48.0%

stays_in_week_nights
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.199968
Minimum0
Maximum25
Zeros2366
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:16.605995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.456656
Coefficient of variation (CV)0.66212599
Kurtosis20.149885
Mean2.199968
Median Absolute Deviation (MAD)1
Skewness2.5224895
Sum82490
Variance2.1218466
MonotonicityNot monotonic
2025-01-24T19:00:16.768308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 12541
33.4%
1 9711
25.9%
3 7808
20.8%
4 2910
 
7.8%
0 2366
 
6.3%
5 1623
 
4.3%
6 189
 
0.5%
7 94
 
0.3%
10 76
 
0.2%
8 71
 
0.2%
Other values (14) 107
 
0.3%
ValueCountFrequency (%)
0 2366
 
6.3%
1 9711
25.9%
2 12541
33.4%
3 7808
20.8%
4 2910
 
7.8%
5 1623
 
4.3%
6 189
 
0.5%
7 94
 
0.3%
8 71
 
0.2%
9 37
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
22 3
 
< 0.1%
21 6
< 0.1%
20 4
< 0.1%
19 2
 
< 0.1%
18 5
< 0.1%
17 3
 
< 0.1%
16 2
 
< 0.1%
15 8
< 0.1%
14 8
< 0.1%

adults
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2
27406 
1
7493 
3
 
2444
0
 
137
4
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37496
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 27406
73.1%
1 7493
 
20.0%
3 2444
 
6.5%
0 137
 
0.4%
4 16
 
< 0.1%

Length

2025-01-24T19:00:16.928270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:17.041683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 27406
73.1%
1 7493
 
20.0%
3 2444
 
6.5%
0 137
 
0.4%
4 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 27406
73.1%
1 7493
 
20.0%
3 2444
 
6.5%
0 137
 
0.4%
4 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 27406
73.1%
1 7493
 
20.0%
3 2444
 
6.5%
0 137
 
0.4%
4 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 27406
73.1%
1 7493
 
20.0%
3 2444
 
6.5%
0 137
 
0.4%
4 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 27406
73.1%
1 7493
 
20.0%
3 2444
 
6.5%
0 137
 
0.4%
4 16
 
< 0.1%

children
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
34838 
1
 
1590
2
 
1050
3
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37496
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 34838
92.9%
1 1590
 
4.2%
2 1050
 
2.8%
3 18
 
< 0.1%

Length

2025-01-24T19:00:17.186118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:17.307077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 34838
92.9%
1 1590
 
4.2%
2 1050
 
2.8%
3 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 34838
92.9%
1 1590
 
4.2%
2 1050
 
2.8%
3 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34838
92.9%
1 1590
 
4.2%
2 1050
 
2.8%
3 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34838
92.9%
1 1590
 
4.2%
2 1050
 
2.8%
3 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34838
92.9%
1 1590
 
4.2%
2 1050
 
2.8%
3 18
 
< 0.1%

babies
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
37327 
1
 
164
2
 
4
10
 
1

Length

Max length2
Median length1
Mean length1.0000267
Min length1

Characters and Unicode

Total characters37497
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 37327
99.5%
1 164
 
0.4%
2 4
 
< 0.1%
10 1
 
< 0.1%

Length

2025-01-24T19:00:17.454409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:17.567127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 37327
99.5%
1 164
 
0.4%
2 4
 
< 0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 37328
99.5%
1 165
 
0.4%
2 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37328
99.5%
1 165
 
0.4%
2 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37328
99.5%
1 165
 
0.4%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37328
99.5%
1 165
 
0.4%
2 4
 
< 0.1%

meal
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
BB
29847 
SC
4952 
HB
 
2696
FB
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters74992
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 29847
79.6%
SC 4952
 
13.2%
HB 2696
 
7.2%
FB 1
 
< 0.1%

Length

2025-01-24T19:00:17.702219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:17.834185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bb 29847
79.6%
sc 4952
 
13.2%
hb 2696
 
7.2%
fb 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 62391
83.2%
S 4952
 
6.6%
C 4952
 
6.6%
H 2696
 
3.6%
F 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 62391
83.2%
S 4952
 
6.6%
C 4952
 
6.6%
H 2696
 
3.6%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 62391
83.2%
S 4952
 
6.6%
C 4952
 
6.6%
H 2696
 
3.6%
F 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 62391
83.2%
S 4952
 
6.6%
C 4952
 
6.6%
H 2696
 
3.6%
F 1
 
< 0.1%
Distinct145
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-01-24T19:00:18.092176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.993626
Min length2

Characters and Unicode

Total characters112249
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.1%

Sample

1st rowESP
2nd rowPRT
3rd rowPRT
4th rowBRA
5th rowPRT
ValueCountFrequency (%)
prt 13534
36.1%
fra 4416
 
11.8%
deu 3166
 
8.4%
gbr 2393
 
6.4%
esp 2261
 
6.0%
ita 1713
 
4.6%
bel 946
 
2.5%
bra 875
 
2.3%
nld 846
 
2.3%
usa 737
 
2.0%
Other values (135) 6609
17.6%
2025-01-24T19:00:18.496478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 23293
20.8%
P 16311
14.5%
T 16132
14.4%
A 9062
 
8.1%
E 7649
 
6.8%
U 5411
 
4.8%
F 4602
 
4.1%
B 4415
 
3.9%
D 4309
 
3.8%
S 4160
 
3.7%
Other values (18) 16905
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 23293
20.8%
P 16311
14.5%
T 16132
14.4%
A 9062
 
8.1%
E 7649
 
6.8%
U 5411
 
4.8%
F 4602
 
4.1%
B 4415
 
3.9%
D 4309
 
3.8%
S 4160
 
3.7%
Other values (18) 16905
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 23293
20.8%
P 16311
14.5%
T 16132
14.4%
A 9062
 
8.1%
E 7649
 
6.8%
U 5411
 
4.8%
F 4602
 
4.1%
B 4415
 
3.9%
D 4309
 
3.8%
S 4160
 
3.7%
Other values (18) 16905
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 23293
20.8%
P 16311
14.5%
T 16132
14.4%
A 9062
 
8.1%
E 7649
 
6.8%
U 5411
 
4.8%
F 4602
 
4.1%
B 4415
 
3.9%
D 4309
 
3.8%
S 4160
 
3.7%
Other values (18) 16905
15.1%

market_segment
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Online TA
19579 
Offline TA/TO
8874 
Groups
4632 
Direct
2658 
Corporate
 
1616
Other values (2)
 
137

Length

Max length13
Median length9
Mean length9.3613452
Min length6

Characters and Unicode

Total characters351013
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline TA
2nd rowDirect
3rd rowDirect
4th rowOnline TA
5th rowOffline TA/TO

Common Values

ValueCountFrequency (%)
Online TA 19579
52.2%
Offline TA/TO 8874
23.7%
Groups 4632
 
12.4%
Direct 2658
 
7.1%
Corporate 1616
 
4.3%
Aviation 125
 
0.3%
Complementary 12
 
< 0.1%

Length

2025-01-24T19:00:18.635556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:18.763022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
online 19579
29.7%
ta 19579
29.7%
offline 8874
13.5%
ta/to 8874
13.5%
groups 4632
 
7.0%
direct 2658
 
4.0%
corporate 1616
 
2.5%
aviation 125
 
0.2%
complementary 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 48169
13.7%
O 37327
10.6%
T 37327
10.6%
e 32751
9.3%
i 31361
8.9%
A 28578
8.1%
l 28465
8.1%
28453
8.1%
f 17748
 
5.1%
r 10534
 
3.0%
Other values (14) 50300
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 48169
13.7%
O 37327
10.6%
T 37327
10.6%
e 32751
9.3%
i 31361
8.9%
A 28578
8.1%
l 28465
8.1%
28453
8.1%
f 17748
 
5.1%
r 10534
 
3.0%
Other values (14) 50300
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 48169
13.7%
O 37327
10.6%
T 37327
10.6%
e 32751
9.3%
i 31361
8.9%
A 28578
8.1%
l 28465
8.1%
28453
8.1%
f 17748
 
5.1%
r 10534
 
3.0%
Other values (14) 50300
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 48169
13.7%
O 37327
10.6%
T 37327
10.6%
e 32751
9.3%
i 31361
8.9%
A 28578
8.1%
l 28465
8.1%
28453
8.1%
f 17748
 
5.1%
r 10534
 
3.0%
Other values (14) 50300
14.3%

distribution_channel
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
TA/TO
32839 
Direct
 
2750
Corporate
 
1806
GDS
 
101

Length

Max length9
Median length5
Mean length5.2606145
Min length3

Characters and Unicode

Total characters197252
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA/TO
2nd rowDirect
3rd rowDirect
4th rowTA/TO
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 32839
87.6%
Direct 2750
 
7.3%
Corporate 1806
 
4.8%
GDS 101
 
0.3%

Length

2025-01-24T19:00:18.961690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:19.078557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 32839
87.6%
direct 2750
 
7.3%
corporate 1806
 
4.8%
gds 101
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T 65678
33.3%
A 32839
16.6%
/ 32839
16.6%
O 32839
16.6%
r 6362
 
3.2%
e 4556
 
2.3%
t 4556
 
2.3%
o 3612
 
1.8%
D 2851
 
1.4%
i 2750
 
1.4%
Other values (6) 8370
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 65678
33.3%
A 32839
16.6%
/ 32839
16.6%
O 32839
16.6%
r 6362
 
3.2%
e 4556
 
2.3%
t 4556
 
2.3%
o 3612
 
1.8%
D 2851
 
1.4%
i 2750
 
1.4%
Other values (6) 8370
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 65678
33.3%
A 32839
16.6%
/ 32839
16.6%
O 32839
16.6%
r 6362
 
3.2%
e 4556
 
2.3%
t 4556
 
2.3%
o 3612
 
1.8%
D 2851
 
1.4%
i 2750
 
1.4%
Other values (6) 8370
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 65678
33.3%
A 32839
16.6%
/ 32839
16.6%
O 32839
16.6%
r 6362
 
3.2%
e 4556
 
2.3%
t 4556
 
2.3%
o 3612
 
1.8%
D 2851
 
1.4%
i 2750
 
1.4%
Other values (6) 8370
 
4.2%

is_repeated_guest
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
36811 
1
 
685

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37496
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36811
98.2%
1 685
 
1.8%

Length

2025-01-24T19:00:19.218339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:19.722961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 36811
98.2%
1 685
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 36811
98.2%
1 685
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36811
98.2%
1 685
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36811
98.2%
1 685
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36811
98.2%
1 685
 
1.8%

previous_cancellations
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056272669
Minimum0
Maximum13
Zeros36039
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:19.812019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4645383
Coefficient of variation (CV)8.2551318
Kurtosis456.75577
Mean0.056272669
Median Absolute Deviation (MAD)0
Skewness19.264955
Sum2110
Variance0.21579583
MonotonicityNot monotonic
2025-01-24T19:00:19.974598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 36039
96.1%
1 1329
 
3.5%
3 38
 
0.1%
11 33
 
0.1%
2 24
 
0.1%
5 11
 
< 0.1%
13 11
 
< 0.1%
6 7
 
< 0.1%
4 4
 
< 0.1%
ValueCountFrequency (%)
0 36039
96.1%
1 1329
 
3.5%
2 24
 
0.1%
3 38
 
0.1%
4 4
 
< 0.1%
5 11
 
< 0.1%
6 7
 
< 0.1%
11 33
 
0.1%
13 11
 
< 0.1%
ValueCountFrequency (%)
13 11
 
< 0.1%
11 33
 
0.1%
6 7
 
< 0.1%
5 11
 
< 0.1%
4 4
 
< 0.1%
3 38
 
0.1%
2 24
 
0.1%
1 1329
 
3.5%
0 36039
96.1%

previous_bookings_not_canceled
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct59
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13385428
Minimum0
Maximum58
Zeros36742
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:20.154377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6818984
Coefficient of variation (CV)12.565145
Kurtosis518.78492
Mean0.13385428
Median Absolute Deviation (MAD)0
Skewness20.690455
Sum5019
Variance2.8287821
MonotonicityNot monotonic
2025-01-24T19:00:20.400038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36742
98.0%
1 263
 
0.7%
2 92
 
0.2%
3 62
 
0.2%
4 62
 
0.2%
5 47
 
0.1%
6 31
 
0.1%
7 18
 
< 0.1%
8 18
 
< 0.1%
9 16
 
< 0.1%
Other values (49) 145
 
0.4%
ValueCountFrequency (%)
0 36742
98.0%
1 263
 
0.7%
2 92
 
0.2%
3 62
 
0.2%
4 62
 
0.2%
5 47
 
0.1%
6 31
 
0.1%
7 18
 
< 0.1%
8 18
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
58 1
< 0.1%
57 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%
49 1
< 0.1%

reserved_room_type
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
A
29192 
D
5986 
F
 
901
B
 
641
E
 
575
Other values (2)
 
201

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37496
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowD

Common Values

ValueCountFrequency (%)
A 29192
77.9%
D 5986
 
16.0%
F 901
 
2.4%
B 641
 
1.7%
E 575
 
1.5%
G 194
 
0.5%
C 7
 
< 0.1%

Length

2025-01-24T19:00:20.592074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:20.716397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 29192
77.9%
d 5986
 
16.0%
f 901
 
2.4%
b 641
 
1.7%
e 575
 
1.5%
g 194
 
0.5%
c 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 29192
77.9%
D 5986
 
16.0%
F 901
 
2.4%
B 641
 
1.7%
E 575
 
1.5%
G 194
 
0.5%
C 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 29192
77.9%
D 5986
 
16.0%
F 901
 
2.4%
B 641
 
1.7%
E 575
 
1.5%
G 194
 
0.5%
C 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 29192
77.9%
D 5986
 
16.0%
F 901
 
2.4%
B 641
 
1.7%
E 575
 
1.5%
G 194
 
0.5%
C 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 29192
77.9%
D 5986
 
16.0%
F 901
 
2.4%
B 641
 
1.7%
E 575
 
1.5%
G 194
 
0.5%
C 7
 
< 0.1%

assigned_room_type
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
A
26505 
D
7373 
B
 
1227
F
 
997
E
 
938
Other values (3)
 
456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37496
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowD

Common Values

ValueCountFrequency (%)
A 26505
70.7%
D 7373
 
19.7%
B 1227
 
3.3%
F 997
 
2.7%
E 938
 
2.5%
G 309
 
0.8%
K 74
 
0.2%
C 73
 
0.2%

Length

2025-01-24T19:00:20.884777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:21.029358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 26505
70.7%
d 7373
 
19.7%
b 1227
 
3.3%
f 997
 
2.7%
e 938
 
2.5%
g 309
 
0.8%
k 74
 
0.2%
c 73
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 26505
70.7%
D 7373
 
19.7%
B 1227
 
3.3%
F 997
 
2.7%
E 938
 
2.5%
G 309
 
0.8%
K 74
 
0.2%
C 73
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 26505
70.7%
D 7373
 
19.7%
B 1227
 
3.3%
F 997
 
2.7%
E 938
 
2.5%
G 309
 
0.8%
K 74
 
0.2%
C 73
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 26505
70.7%
D 7373
 
19.7%
B 1227
 
3.3%
F 997
 
2.7%
E 938
 
2.5%
G 309
 
0.8%
K 74
 
0.2%
C 73
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 26505
70.7%
D 7373
 
19.7%
B 1227
 
3.3%
F 997
 
2.7%
E 938
 
2.5%
G 309
 
0.8%
K 74
 
0.2%
C 73
 
0.2%

booking_changes
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18716663
Minimum0
Maximum17
Zeros32735
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:21.188549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61036224
Coefficient of variation (CV)3.2610633
Kurtosis89.777524
Mean0.18716663
Median Absolute Deviation (MAD)0
Skewness6.6067359
Sum7018
Variance0.37254206
MonotonicityNot monotonic
2025-01-24T19:00:21.354267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 32735
87.3%
1 3272
 
8.7%
2 1094
 
2.9%
3 237
 
0.6%
4 90
 
0.2%
5 24
 
0.1%
6 15
 
< 0.1%
7 11
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
Other values (5) 9
 
< 0.1%
ValueCountFrequency (%)
0 32735
87.3%
1 3272
 
8.7%
2 1094
 
2.9%
3 237
 
0.6%
4 90
 
0.2%
5 24
 
0.1%
6 15
 
< 0.1%
7 11
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 5
 
< 0.1%
7 11
< 0.1%
6 15
< 0.1%
5 24
0.1%

deposit_type
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
No Deposit
31587 
Non Refund
5906 
Refundable
 
3

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters374960
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 31587
84.2%
Non Refund 5906
 
15.8%
Refundable 3
 
< 0.1%

Length

2025-01-24T19:00:21.545729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:21.666262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31587
42.1%
deposit 31587
42.1%
non 5906
 
7.9%
refund 5906
 
7.9%
refundable 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 69080
18.4%
e 37499
10.0%
N 37493
10.0%
37493
10.0%
s 31587
8.4%
i 31587
8.4%
t 31587
8.4%
p 31587
8.4%
D 31587
8.4%
n 11815
 
3.2%
Other values (7) 23645
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 69080
18.4%
e 37499
10.0%
N 37493
10.0%
37493
10.0%
s 31587
8.4%
i 31587
8.4%
t 31587
8.4%
p 31587
8.4%
D 31587
8.4%
n 11815
 
3.2%
Other values (7) 23645
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 69080
18.4%
e 37499
10.0%
N 37493
10.0%
37493
10.0%
s 31587
8.4%
i 31587
8.4%
t 31587
8.4%
p 31587
8.4%
D 31587
8.4%
n 11815
 
3.2%
Other values (7) 23645
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 69080
18.4%
e 37499
10.0%
N 37493
10.0%
37493
10.0%
s 31587
8.4%
i 31587
8.4%
t 31587
8.4%
p 31587
8.4%
D 31587
8.4%
n 11815
 
3.2%
Other values (7) 23645
 
6.3%

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct86
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5678206
Minimum0
Maximum391
Zeros34846
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:21.848070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile39
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.674532
Coefficient of variation (CV)5.1500459
Kurtosis76.951094
Mean5.5678206
Median Absolute Deviation (MAD)0
Skewness7.8856975
Sum208771
Variance822.22876
MonotonicityNot monotonic
2025-01-24T19:00:22.088435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34846
92.9%
39 225
 
0.6%
44 136
 
0.4%
31 123
 
0.3%
35 94
 
0.3%
46 87
 
0.2%
63 80
 
0.2%
45 65
 
0.2%
38 64
 
0.2%
41 63
 
0.2%
Other values (76) 1713
 
4.6%
ValueCountFrequency (%)
0 34846
92.9%
1 5
 
< 0.1%
2 1
 
< 0.1%
3 59
 
0.2%
4 20
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
8 4
 
< 0.1%
9 13
 
< 0.1%
10 27
 
0.1%
ValueCountFrequency (%)
391 45
0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
0.1%
224 10
 
< 0.1%
223 60
0.2%
215 21
 
0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Transient
29798 
Transient-Party
7465 
Contract
 
177
Group
 
56

Length

Max length15
Median length9
Mean length10.183833
Min length5

Characters and Unicode

Total characters381853
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient-Party
3rd rowTransient-Party
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 29798
79.5%
Transient-Party 7465
 
19.9%
Contract 177
 
0.5%
Group 56
 
0.1%

Length

2025-01-24T19:00:22.291744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:22.397949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transient 29798
79.5%
transient-party 7465
 
19.9%
contract 177
 
0.5%
group 56
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 74703
19.6%
t 45082
11.8%
r 44961
11.8%
a 44905
11.8%
T 37263
9.8%
s 37263
9.8%
i 37263
9.8%
e 37263
9.8%
y 7465
 
2.0%
- 7465
 
2.0%
Other values (7) 8220
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 381853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 74703
19.6%
t 45082
11.8%
r 44961
11.8%
a 44905
11.8%
T 37263
9.8%
s 37263
9.8%
i 37263
9.8%
e 37263
9.8%
y 7465
 
2.0%
- 7465
 
2.0%
Other values (7) 8220
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 381853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 74703
19.6%
t 45082
11.8%
r 44961
11.8%
a 44905
11.8%
T 37263
9.8%
s 37263
9.8%
i 37263
9.8%
e 37263
9.8%
y 7465
 
2.0%
- 7465
 
2.0%
Other values (7) 8220
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 381853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 74703
19.6%
t 45082
11.8%
r 44961
11.8%
a 44905
11.8%
T 37263
9.8%
s 37263
9.8%
i 37263
9.8%
e 37263
9.8%
y 7465
 
2.0%
- 7465
 
2.0%
Other values (7) 8220
 
2.2%

adr
Real number (ℝ)

Distinct3921
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.09422
Minimum21
Maximum451.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:22.576305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile62
Q180
median99.48
Q3122.65
95-th percentile168.3
Maximum451.5
Range430.5
Interquartile range (IQR)42.65

Descriptive statistics

Standard deviation33.840805
Coefficient of variation (CV)0.32200442
Kurtosis2.7761707
Mean105.09422
Median Absolute Deviation (MAD)20.52
Skewness1.2021844
Sum3940613
Variance1145.2001
MonotonicityIncreasing
2025-01-24T19:00:22.773301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 1226
 
3.3%
65 1069
 
2.9%
62 993
 
2.6%
110 783
 
2.1%
120 777
 
2.1%
90 766
 
2.0%
95 731
 
1.9%
100 688
 
1.8%
115 669
 
1.8%
80 598
 
1.6%
Other values (3911) 29196
77.9%
ValueCountFrequency (%)
21 1
 
< 0.1%
22.5 2
 
< 0.1%
23 1
 
< 0.1%
26.35 1
 
< 0.1%
31 11
< 0.1%
32.5 4
 
< 0.1%
32.85 1
 
< 0.1%
35 2
 
< 0.1%
37.33 1
 
< 0.1%
37.5 1
 
< 0.1%
ValueCountFrequency (%)
451.5 1
< 0.1%
375.5 1
< 0.1%
365 1
< 0.1%
349.63 1
< 0.1%
345 1
< 0.1%
332.57 1
< 0.1%
316 1
< 0.1%
314.1 1
< 0.1%
309.5 1
< 0.1%
306 2
< 0.1%

required_car_parking_spaces
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
36330 
1
 
1164
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37496
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36330
96.9%
1 1164
 
3.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Length

2025-01-24T19:00:22.948992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:23.065347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 36330
96.9%
1 1164
 
3.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36330
96.9%
1 1164
 
3.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36330
96.9%
1 1164
 
3.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36330
96.9%
1 1164
 
3.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36330
96.9%
1 1164
 
3.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54896522
Minimum0
Maximum5
Zeros22139
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:23.174027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75632374
Coefficient of variation (CV)1.3777261
Kurtosis1.4133124
Mean0.54896522
Median Absolute Deviation (MAD)0
Skewness1.3144796
Sum20584
Variance0.5720256
MonotonicityNot monotonic
2025-01-24T19:00:23.292584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 22139
59.0%
1 10887
29.0%
2 3802
 
10.1%
3 588
 
1.6%
4 71
 
0.2%
5 9
 
< 0.1%
ValueCountFrequency (%)
0 22139
59.0%
1 10887
29.0%
2 3802
 
10.1%
3 588
 
1.6%
4 71
 
0.2%
5 9
 
< 0.1%
ValueCountFrequency (%)
5 9
 
< 0.1%
4 71
 
0.2%
3 588
 
1.6%
2 3802
 
10.1%
1 10887
29.0%
0 22139
59.0%

reservation_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Check-Out
22177 
Canceled
14775 
No-Show
 
544

Length

Max length9
Median length9
Mean length8.5769415
Min length7

Characters and Unicode

Total characters321601
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCanceled

Common Values

ValueCountFrequency (%)
Check-Out 22177
59.1%
Canceled 14775
39.4%
No-Show 544
 
1.5%

Length

2025-01-24T19:00:23.446928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:00:23.555256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
check-out 22177
59.1%
canceled 14775
39.4%
no-show 544
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 51727
16.1%
C 36952
11.5%
c 36952
11.5%
h 22721
7.1%
- 22721
7.1%
u 22177
6.9%
t 22177
6.9%
O 22177
6.9%
k 22177
6.9%
a 14775
 
4.6%
Other values (7) 47045
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 321601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 51727
16.1%
C 36952
11.5%
c 36952
11.5%
h 22721
7.1%
- 22721
7.1%
u 22177
6.9%
t 22177
6.9%
O 22177
6.9%
k 22177
6.9%
a 14775
 
4.6%
Other values (7) 47045
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 321601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 51727
16.1%
C 36952
11.5%
c 36952
11.5%
h 22721
7.1%
- 22721
7.1%
u 22177
6.9%
t 22177
6.9%
O 22177
6.9%
k 22177
6.9%
a 14775
 
4.6%
Other values (7) 47045
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 321601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 51727
16.1%
C 36952
11.5%
c 36952
11.5%
h 22721
7.1%
- 22721
7.1%
u 22177
6.9%
t 22177
6.9%
O 22177
6.9%
k 22177
6.9%
a 14775
 
4.6%
Other values (7) 47045
14.6%
Distinct438
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size293.1 KiB
Minimum2015-01-12 00:00:00
Maximum2017-08-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-24T19:00:23.716591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:23.953776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_stay
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.981518
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:24.305807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum34
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8653585
Coefficient of variation (CV)0.62564053
Kurtosis28.655002
Mean2.981518
Median Absolute Deviation (MAD)1
Skewness3.3631401
Sum111795
Variance3.4795624
MonotonicityNot monotonic
2025-01-24T19:00:24.554611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 10389
27.7%
2 9669
25.8%
1 6772
18.1%
4 5629
15.0%
5 2404
 
6.4%
6 1033
 
2.8%
7 990
 
2.6%
8 181
 
0.5%
10 108
 
0.3%
9 98
 
0.3%
Other values (21) 223
 
0.6%
ValueCountFrequency (%)
1 6772
18.1%
2 9669
25.8%
3 10389
27.7%
4 5629
15.0%
5 2404
 
6.4%
6 1033
 
2.8%
7 990
 
2.6%
8 181
 
0.5%
9 98
 
0.3%
10 108
 
0.3%
ValueCountFrequency (%)
34 1
 
< 0.1%
30 3
 
< 0.1%
29 8
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
 
< 0.1%
25 1
 
< 0.1%
24 4
< 0.1%
23 2
 
< 0.1%
22 2
 
< 0.1%

cancellation_ratio
Real number (ℝ)

High correlation  Zeros 

Distinct103
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03516661
Minimum0
Maximum0.99999923
Zeros36039
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size293.1 KiB
2025-01-24T19:00:24.929412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.99999923
Range0.99999923
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18230625
Coefficient of variation (CV)5.1840723
Kurtosis23.779931
Mean0.03516661
Median Absolute Deviation (MAD)0
Skewness5.0670277
Sum1318.6072
Variance0.033235569
MonotonicityNot monotonic
2025-01-24T19:00:25.371335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36039
96.1%
0.9999900001 1256
 
3.3%
0.9285707653 10
 
< 0.1%
0.1666663889 9
 
< 0.1%
0.1428569388 8
 
< 0.1%
0.3055554707 8
 
< 0.1%
0.249999375 8
 
< 0.1%
0.1999996 8
 
< 0.1%
0.7333328444 7
 
< 0.1%
0.4999975 7
 
< 0.1%
Other values (93) 136
 
0.4%
ValueCountFrequency (%)
0 36039
96.1%
0.03333332222 1
 
< 0.1%
0.03448274673 1
 
< 0.1%
0.05263155125 1
 
< 0.1%
0.05555552469 1
 
< 0.1%
0.05882349481 1
 
< 0.1%
0.06249996094 2
 
< 0.1%
0.06382977365 1
 
< 0.1%
0.06521737713 1
 
< 0.1%
0.06666662222 1
 
< 0.1%
ValueCountFrequency (%)
0.9999992308 1
 
< 0.1%
0.9999990909 3
 
< 0.1%
0.9999983333 6
 
< 0.1%
0.9999966667 1
 
< 0.1%
0.9999900001 1256
3.3%
0.9285707653 10
 
< 0.1%
0.9166659028 1
 
< 0.1%
0.749998125 1
 
< 0.1%
0.7333328444 7
 
< 0.1%
0.6874995703 5
 
< 0.1%

Interactions

2025-01-24T19:00:09.379381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:37.871971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:40.268982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:42.802051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:46.452403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:48.640176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:51.088134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:53.328933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:55.677835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:59.555573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:02.217987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:04.434530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:06.741905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:09.559463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:38.050060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:40.440510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:43.096260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:46.637518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:48.799595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T18:59:53.497205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:55.870176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:59.810981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T18:59:48.964488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:51.428172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:53.712464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:00:09.929466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T18:59:52.821574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:55.097287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:58.669929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:01.640550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:03.916449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:06.216999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:08.868464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:12.015338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:39.891854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:42.251180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:46.098842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:48.253876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:50.765634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:52.981547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:55.278777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:58.949989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:01.832471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:04.075691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:06.377273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:09.032070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:12.358158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:40.087182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:42.500806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:46.263484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:48.450158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:50.927676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:53.161755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:55.469780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T18:59:59.259604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:02.042576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:04.277419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:06.570327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:00:09.200729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-24T19:00:25.695566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adradultsarrival_date_day_of_montharrival_date_montharrival_date_week_numberassigned_room_typebabiesbooking_changescancellation_ratiochildrencustomer_typedays_in_waiting_listdeposit_typedistribution_channelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requeststotal_stay
adr1.0000.183-0.0080.1250.1550.2680.0470.021-0.1380.3070.092-0.1220.2290.0910.1510.091-0.1890.1930.109-0.125-0.1390.0640.1070.3190.0110.0280.2340.025
adults0.1831.0000.0260.1060.1020.2670.0190.0210.0460.1920.0910.0330.0820.1800.0560.2090.0920.1880.0800.0680.0390.0150.0650.3190.0500.0590.1150.045
arrival_date_day_of_month-0.0080.0261.0000.0850.0220.0110.0110.0210.0140.0230.0340.0320.0660.0360.0450.0220.0090.0600.039-0.0120.0140.0090.0360.016-0.0110.007-0.003-0.006
arrival_date_month0.1250.1060.0851.0000.8160.0410.0170.0240.0850.0880.0920.1090.0950.0710.0810.0720.1700.1080.0720.0280.0490.0470.0900.0610.0460.0490.0790.050
arrival_date_week_number0.1550.1020.0220.8161.0000.0370.0160.008-0.1580.0810.103-0.1340.1030.0670.0820.0750.1740.0960.063-0.005-0.1570.0440.0870.0550.0490.0330.1230.052
assigned_room_type0.2680.2670.0110.0410.0371.0000.0420.0300.0390.4180.0990.0430.1880.1300.1760.0290.0870.1460.1170.0220.0000.0460.1300.7040.0390.0360.0890.040
babies0.0470.0190.0110.0170.0160.0421.0000.0340.0000.0230.0120.0000.0190.0320.0290.0020.0160.0370.0440.0000.0000.0150.0200.0490.0000.0000.0550.000
booking_changes0.0210.0210.0210.0240.0080.0300.0341.000-0.0570.0430.034-0.0430.0590.0340.0720.000-0.0260.0300.0000.039-0.0560.0150.0530.0220.0200.0290.0460.033
cancellation_ratio-0.1380.0460.0140.085-0.1580.0390.000-0.0571.0000.0260.0750.3400.2470.1150.2230.3790.0990.1250.0450.1531.0000.0220.1630.034-0.028-0.076-0.125-0.065
children0.3070.1920.0230.0880.0810.4180.0230.0430.0261.0000.0490.0290.0840.0530.0340.0310.0580.1170.0500.0000.0000.0410.0250.4600.0220.0300.0930.034
customer_type0.0920.0910.0340.0920.1030.0990.0120.0340.0750.0491.0000.1070.1500.0740.1870.0750.1130.2230.1510.0140.0210.0520.1360.1110.0310.0500.0740.045
days_in_waiting_list-0.1220.0330.0320.109-0.1340.0430.000-0.0430.3400.0290.1071.0000.2080.0450.1560.0260.2760.1360.083-0.0150.3370.0170.1150.0440.030-0.097-0.187-0.022
deposit_type0.2290.0820.0660.0950.1030.1880.0190.0590.2470.0840.1500.2081.0000.1070.5170.0570.3840.4440.1180.0130.0220.0540.3770.1560.0730.1040.2520.074
distribution_channel0.0910.1800.0360.0710.0670.1300.0320.0340.1150.0530.0740.0450.1071.0000.1640.4530.1350.7350.0830.1750.1080.0740.1240.1640.0710.0650.0630.057
is_canceled0.1510.0560.0450.0810.0820.1760.0290.0720.2230.0340.1870.1560.5170.1641.0000.0870.3020.3000.0490.0500.0350.1491.0000.0740.0490.0710.3930.053
is_repeated_guest0.0910.2090.0220.0720.0750.0290.0020.0000.3790.0310.0750.0260.0570.4530.0871.0000.1310.4790.0500.5180.3770.1290.0890.0390.0680.0510.0250.043
lead_time-0.1890.0920.0090.1700.1740.0870.016-0.0260.0990.0580.1130.2760.3840.1350.3020.1311.0000.2460.149-0.1480.0970.0510.2270.0860.2190.005-0.1470.185
market_segment0.1930.1880.0600.1080.0960.1460.0370.0300.1250.1170.2230.1360.4440.7350.3000.4790.2461.0000.2220.1310.0810.0950.2240.1820.0910.1170.2010.096
meal0.1090.0800.0390.0720.0630.1170.0440.0000.0450.0500.1510.0830.1180.0830.0490.0500.1490.2221.0000.0140.0100.0050.0420.1240.0400.0140.0650.020
previous_bookings_not_canceled-0.1250.068-0.0120.028-0.0050.0220.0000.0390.1530.0000.014-0.0150.0130.1750.0500.518-0.1480.1310.0141.0000.1640.0390.0370.028-0.114-0.0480.007-0.127
previous_cancellations-0.1390.0390.0140.049-0.1570.0000.000-0.0561.0000.0000.0210.3370.0220.1080.0350.3770.0970.0810.0100.1641.0000.0180.0240.000-0.029-0.075-0.124-0.066
required_car_parking_spaces0.0640.0150.0090.0470.0440.0460.0150.0150.0220.0410.0520.0170.0540.0740.1490.1290.0510.0950.0050.0390.0181.0000.1050.0460.0090.0000.0570.000
reservation_status0.1070.0650.0360.0900.0870.1300.0200.0530.1630.0250.1360.1150.3770.1241.0000.0890.2270.2240.0420.0370.0240.1051.0000.0550.0970.1010.2800.100
reserved_room_type0.3190.3190.0160.0610.0550.7040.0490.0220.0340.4600.1110.0440.1560.1640.0740.0390.0860.1820.1240.0280.0000.0460.0551.0000.0530.0460.0880.054
stays_in_week_nights0.0110.050-0.0110.0460.0490.0390.0000.020-0.0280.0220.0310.0300.0730.0710.0490.0680.2190.0910.040-0.114-0.0290.0090.0970.0531.0000.0130.0390.809
stays_in_weekend_nights0.0280.0590.0070.0490.0330.0360.0000.029-0.0760.0300.050-0.0970.1040.0650.0710.0510.0050.1170.014-0.048-0.0750.0000.1010.0460.0131.0000.1000.559
total_of_special_requests0.2340.115-0.0030.0790.1230.0890.0550.046-0.1250.0930.074-0.1870.2520.0630.3930.025-0.1470.2010.0650.007-0.1240.0570.2800.0880.0390.1001.0000.095
total_stay0.0250.045-0.0060.0500.0520.0400.0000.033-0.0650.0340.045-0.0220.0740.0570.0530.0430.1850.0960.020-0.127-0.0660.0000.1000.0540.8090.5590.0951.000

Missing values

2025-01-24T19:00:12.870020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-24T19:00:13.708375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datetotal_staycancellation_ratio
0City Hotel002016October442911200BBESPOnline TATA/TO100AA2No Deposit0Transient21.0004Check-Out31/10/201620.0
1City Hotel002016February6302200BBPRTDirectDirect000BB1No Deposit0Transient-Party22.5010Check-Out05/02/201620.0
2City Hotel002016February6302100BBPRTDirectDirect000BB1No Deposit0Transient-Party22.5000Check-Out05/02/201620.0
3City Hotel0572016February7812200BBBRAOnline TATA/TO000BB0No Deposit0Transient23.0000Check-Out11/02/201630.0
4City Hotel1312016April172324210BBPRTOffline TA/TOTA/TO000DD0No Deposit0Transient26.3500Canceled23/03/201660.0
5City Hotel032016February92601200BBPRTCorporateCorporate000AA2No Deposit0Transient-Party31.0001Check-Out27/02/201610.0
6City Hotel0162016February92601100BBESPCorporateCorporate000AA1No Deposit0Transient-Party31.0001Check-Out27/02/201610.0
7City Hotel0162016February92601100BBESPCorporateCorporate000AE1No Deposit0Transient-Party31.0002Check-Out27/02/201610.0
8City Hotel0162016February92601100BBPRTCorporateCorporate000AA1No Deposit0Transient-Party31.0001Check-Out27/02/201610.0
9City Hotel032016February92601100BBESPCorporateCorporate000AA1No Deposit0Transient-Party31.0001Check-Out27/02/201610.0
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datetotal_staycancellation_ratio
37486City Hotel042016July28812220BBMEXOnline TATA/TO000GG0No Deposit0Transient306.0003Check-Out11/07/201630.0
37487City Hotel02062016August32501220HBGBRDirectDirect000GG1No Deposit0Transient309.5010Check-Out06/08/201610.0
37488City Hotel0572016December533012200HBFRAOnline TATA/TO000DD0No Deposit0Transient314.1000Check-Out02/01/201730.0
37489City Hotel062016August331301220HBIRLOnline TATA/TO000FF0No Deposit0Transient316.0010Check-Out14/08/201610.0
37490City Hotel0282016June23203220BBPRTOnline TATA/TO000FG0No Deposit0Transient332.5701Check-Out05/06/201630.0
37491City Hotel0212016September37704310HBPRTDirectDirect000GG0No Deposit0Transient345.0010Check-Out11/09/201640.0
37492City Hotel0432016December532903220HBBELOnline TATA/TO000FF0No Deposit0Transient349.6301Check-Out01/01/201730.0
37493City Hotel01732016July312512210HBFRAOffline TA/TOTA/TO000AD0No Deposit0Transient-Party365.0001Check-Out28/07/201630.0
37494City Hotel0212016December533002300BBFRAOnline TATA/TO000DD0No Deposit0Transient375.5000Check-Out01/01/201720.0
37495City Hotel0812016December533111220BBPRTDirectDirect000EE1No Deposit0Transient-Party451.5004Check-Out02/01/201720.0

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datetotal_staycancellation_ratio# duplicates
2483City Hotel12772016November46712200BBPRTGroupsTA/TO000AA0Non Refund0Transient100.000Canceled04/04/201630.00000180
1917City Hotel1682016February81702200BBPRTGroupsTA/TO010AA0Non Refund0Transient75.000Canceled06/01/201620.99999150
2326City Hotel11882016June251502100BBPRTOffline TA/TOTA/TO000AA0Non Refund39Transient130.000Canceled18/01/201620.00000109
2234City Hotel11582016May222402100BBPRTGroupsTA/TO000AA0Non Refund31Transient130.000Canceled18/01/201620.00000101
1929City Hotel1712016June251403100BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient120.000Canceled27/04/201630.0000089
2262City Hotel11662016November45103100BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient110.000Canceled13/07/201630.0000085
2524City Hotel13042016November45303200BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient89.000Canceled01/02/201630.0000085
2525City Hotel13052016November45412200BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient89.000Canceled01/02/201630.0000085
1786City Hotel1372016October421303200BBPRTOffline TA/TOTA/TO000AA0No Deposit0Transient-Party105.000Canceled06/09/201630.0000084
2043City Hotel1992016February81901200BBPRTCorporateCorporate010AA0No Deposit0Transient-Party80.000Canceled22/12/201510.9999969